one noise variable, linear regression

## [1] "*************************************************************"
## [1] "one noise variable, linear regression"
## [1] "bSigmaBest 26"
## [1] "naive effects model"
## [1] "one noise variable, linear regression naive effects model fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2322 -0.6020  0.0120  0.5804  3.2574 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.001467   0.019623   0.075     0.94    
## n1          1.000321   0.038697  25.850   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8776 on 1998 degrees of freedom
## Multiple R-squared:  0.2506, Adjusted R-squared:  0.2503 
## F-statistic: 668.2 on 1 and 1998 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 0.87711349635425"
## [1] " application rmse 1.15239485807949"
## [1] "one noise variable, linear regression naive effects model train rmse 0.87711349635425"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                grob
## 1 1 (2-2,1-1) arrange      gtable[layout]
## 2 2 (2-2,2-2) arrange      gtable[layout]
## 3 3 (3-3,1-1) arrange      gtable[layout]
## 4 4 (3-3,2-2) arrange      gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.136]
## [1] "one noise variable, linear regression naive effects model test rmse 1.15239485807949"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                grob
## 1 1 (2-2,1-1) arrange      gtable[layout]
## 2 2 (2-2,2-2) arrange      gtable[layout]
## 3 3 (3-3,1-1) arrange      gtable[layout]
## 4 4 (3-3,2-2) arrange      gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.285]
## [1] "effects model, sigma= 26"
## [1] "one noise variable, linear regression effects model, sigma= 26 fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4199 -0.6771 -0.0035  0.6637  3.8867 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.002211   0.022739   0.097    0.923
## n1          0.001391   0.003372   0.412    0.680
## 
## Residual standard error: 1.014 on 1998 degrees of freedom
## Multiple R-squared:  8.512e-05,  Adjusted R-squared:  -0.0004153 
## F-statistic: 0.1701 on 1 and 1998 DF,  p-value: 0.6801
## 
## [1] " train rmse 1.01318496986114"
## [1] " application rmse 0.995905706944315"
## [1] "one noise variable, linear regression Laplace noised 26 train rmse 1.01318496986114"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                grob
## 1 1 (2-2,1-1) arrange      gtable[layout]
## 2 2 (2-2,2-2) arrange      gtable[layout]
## 3 3 (3-3,1-1) arrange      gtable[layout]
## 4 4 (3-3,2-2) arrange      gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.434]
## [1] "one noise variable, linear regression Laplace noised 26 test rmse 0.995905706944315"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                grob
## 1 1 (2-2,1-1) arrange      gtable[layout]
## 2 2 (2-2,2-2) arrange      gtable[layout]
## 3 3 (3-3,1-1) arrange      gtable[layout]
## 4 4 (3-3,2-2) arrange      gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.583]
## [1] "effects model, jacknifed"
## [1] "one noise variable, linear regression effects model, jackknifed fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4251 -0.6776 -0.0009  0.6645  3.8913 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001465   0.022668   0.065    0.948
## n1          0.004279   0.038189   0.112    0.911
## 
## Residual standard error: 1.014 on 1998 degrees of freedom
## Multiple R-squared:  6.285e-06,  Adjusted R-squared:  -0.0004942 
## F-statistic: 0.01256 on 1 and 1998 DF,  p-value: 0.9108
## 
## [1] " train rmse 1.01322491166252"
## [1] " application rmse 0.99567998170435"
## [1] "one noise variable, linear regression jackknifed train rmse 1.01322491166252"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                grob
## 1 1 (2-2,1-1) arrange      gtable[layout]
## 2 2 (2-2,2-2) arrange      gtable[layout]
## 3 3 (3-3,1-1) arrange      gtable[layout]
## 4 4 (3-3,2-2) arrange      gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.732]
## [1] "one noise variable, linear regression jackknifed test rmse 0.99567998170435"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                grob
## 1 1 (2-2,1-1) arrange      gtable[layout]
## 2 2 (2-2,2-2) arrange      gtable[layout]
## 3 3 (3-3,1-1) arrange      gtable[layout]
## 4 4 (3-3,2-2) arrange      gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.881]

## [1] "********"
## [1] "one noise variable, linear regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9905  0.9968  0.9998  1.0010  1.0030  1.0210 
## [1] 0.007566677
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.127   1.142   1.154   1.154   1.159   1.185 
## [1] 0.01570842
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9905  0.9968  0.9994  1.0010  1.0040  1.0190 
## [1] 0.007766595
## [1] "********"

## [1] "*************************************************************"

one variable, linear regression

## [1] "*************************************************************"
## [1] "one variable, linear regression"
## [1] "bSigmaBest 3"
## [1] "naive effects model"
## [1] "one variable, linear regression naive effects model fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3721 -0.6891 -0.0037  0.6848  3.7826 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20623    0.02260   9.125   <2e-16 ***
## x1           1.00000    0.03685  27.137   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.011 on 1998 degrees of freedom
## Multiple R-squared:  0.2693, Adjusted R-squared:  0.269 
## F-statistic: 736.4 on 1 and 1998 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 1.01025938596012"
## [1] " application rmse 0.999915402747535"
## [1] "one variable, linear regression naive effects model train rmse 1.01025938596012"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1308]
## [1] "one variable, linear regression naive effects model test rmse 0.999915402747535"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1457]
## [1] "effects model, sigma= 3"
## [1] "one variable, linear regression effects model, sigma= 3 fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3804 -0.6915 -0.0012  0.6855  3.7891 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20567    0.02261   9.098   <2e-16 ***
## x1           1.00679    0.03712  27.119   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.011 on 1998 degrees of freedom
## Multiple R-squared:  0.2691, Adjusted R-squared:  0.2687 
## F-statistic: 735.5 on 1 and 1998 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 1.01043998850533"
## [1] " application rmse 1.00077015547304"
## [1] "one variable, linear regression Laplace noised 3 train rmse 1.01043998850533"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1606]
## [1] "one variable, linear regression Laplace noised 3 test rmse 1.00077015547304"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1755]
## [1] "effects model, jacknifed"
## [1] "one variable, linear regression effects model, jackknifed fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3933 -0.6946 -0.0039  0.6875  3.7985 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.2062     0.0227   9.084   <2e-16 ***
## x1            0.9871     0.0370  26.682   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.015 on 1998 degrees of freedom
## Multiple R-squared:  0.2627, Adjusted R-squared:  0.2623 
## F-statistic:   712 on 1 and 1998 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 1.01481235978284"
## [1] " application rmse 1.00008428967326"
## [1] "one variable, linear regression jackknifed train rmse 1.01481235978284"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1904]
## [1] "one variable, linear regression jackknifed test rmse 1.00008428967326"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2053]

## [1] "********"
## [1] "one variable, linear regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9887  0.9988  1.0020  1.0020  1.0050  1.0180 
## [1] 0.006854633
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9887  0.9990  1.0020  1.0020  1.0050  1.0180 
## [1] 0.006824758
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9887  0.9994  1.0030  1.0030  1.0050  1.0180 
## [1] 0.007026908
## [1] "********"

## [1] "*************************************************************"

one variable plus noise variable, linear regression

## [1] "*************************************************************"
## [1] "one variable plus noise variable, linear regression"
## [1] "bSigmaBest 17"
## [1] "naive effects model"
## [1] "one variable plus noise variable, linear regression naive effects model fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9216 -0.6181  0.0055  0.6225  3.5298 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20622    0.02058   10.02   <2e-16 ***
## x1           0.83459    0.03452   24.17   <2e-16 ***
## n1           0.78131    0.03844   20.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9203 on 1997 degrees of freedom
## Multiple R-squared:  0.3946, Adjusted R-squared:  0.394 
## F-statistic: 650.8 on 2 and 1997 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 0.919591353886876"
## [1] " application rmse 1.12246743812363"
## [1] "one variable plus noise variable, linear regression naive effects model train rmse 0.919591353886876"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2480]
## [1] "one variable plus noise variable, linear regression naive effects model test rmse 1.12246743812363"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2629]
## [1] "effects model, sigma= 17"
## [1] "one variable plus noise variable, linear regression effects model, sigma= 17 fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4407 -0.6842  0.0052  0.6823  3.7289 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.216818   0.022652   9.572   <2e-16 ***
## x1          1.002290   0.037240  26.914   <2e-16 ***
## n1          0.006125   0.004126   1.484    0.138    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.013 on 1997 degrees of freedom
## Multiple R-squared:  0.2668, Adjusted R-squared:  0.266 
## F-statistic: 363.3 on 2 and 1997 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 1.01201210045781"
## [1] " application rmse 1.01234721515415"
## [1] "one variable plus noise variable, linear regression Laplace noised 17 train rmse 1.01201210045781"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2778]
## [1] "one variable plus noise variable, linear regression Laplace noised 17 test rmse 1.01234721515415"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2927]
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, linear regression effects model, jackknifed fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3986 -0.6920 -0.0077  0.6877  3.8126 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20643    0.02268   9.101   <2e-16 ***
## x1           0.98425    0.03698  26.614   <2e-16 ***
## n1          -0.07739    0.03479  -2.224   0.0262 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.014 on 1997 degrees of freedom
## Multiple R-squared:  0.2645, Adjusted R-squared:  0.2638 
## F-statistic: 359.2 on 2 and 1997 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 1.01355772650768"
## [1] " application rmse 1.00913108707443"
## [1] "one variable plus noise variable, linear regression jackknifed train rmse 1.01355772650768"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3076]
## [1] "one variable plus noise variable, linear regression jackknifed test rmse 1.00913108707443"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3225]

## [1] "********"
## [1] "one variable plus noise variable, linear regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9862  0.9965  1.0050  1.0020  1.0090  1.0130 
## [1] 0.008962906
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.091   1.119   1.127   1.130   1.142   1.167 
## [1] 0.01843663
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9887  1.0010  1.0100  1.0080  1.0140  1.0200 
## [1] 0.009739627
## [1] "********"

## [1] "*************************************************************"

one variable plus noise variable, diagonal regression

## [1] "*************************************************************"
## [1] "one variable plus noise variable, diagonal regression"
## [1] "bSigmaBest 10"
## [1] "naive effects model"
## [1] "one variable plus noise variable, diagonal regression naive effects model fit model:"
##       x1       n1 
## 1.000005 1.000333 
## [1] " train rmse 0.958540237968956"
## [1] " application rmse 1.20618715828122"
## [1] "one variable plus noise variable, diagonal regression naive effects model train rmse 0.958540237968956"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3652]
## [1] "one variable plus noise variable, diagonal regression naive effects model test rmse 1.20618715828122"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3801]
## [1] "effects model, sigma= 10"
## [1] "one variable plus noise variable, diagonal regression effects model, sigma= 10 fit model:"
##         x1         n1 
## 1.00058530 0.01889345 
## [1] " train rmse 1.03170258962911"
## [1] " application rmse 1.03576497717631"
## [1] "one variable plus noise variable, diagonal regression Laplace noised 10 train rmse 1.03170258962911"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3950]
## [1] "one variable plus noise variable, diagonal regression Laplace noised 10 test rmse 1.03576497717631"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4099]
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, diagonal regression effects model, jackknifed fit model:"
##         x1         n1 
##  0.9871528 -0.1088369 
## [1] " train rmse 1.03458802692346"
## [1] " application rmse 1.03176880530955"
## [1] "one variable plus noise variable, diagonal regression jackknifed train rmse 1.03458802692346"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4248]
## [1] "one variable plus noise variable, diagonal regression jackknifed test rmse 1.03176880530955"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4397]

## [1] "********"
## [1] "one variable plus noise variable, diagonal regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.004   1.012   1.018   1.019   1.024   1.033 
## [1] 0.008702826
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.179   1.205   1.217   1.217   1.227   1.248 
## [1] 0.01815138
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.008   1.019   1.029   1.027   1.032   1.043 
## [1] 0.01027036
## [1] "********"

## [1] "*************************************************************"